Parameter extraction of photovoltaic models using a memory-based improved gorilla troops optimizer

•A Memory-based Improved Gorilla Troops Optimizer (MIGTO) is proposed.•Two improvements are introduced to the proposed MIGTO algorithm.•MIGTO is used to determine unknown parameters of photovoltaic model.•Four different PV models are evaluated using one, two, three diode and PV models.•Comprehensive...

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Bibliographic Details
Published in:Energy conversion and management 2022-01, Vol.252, p.115134, Article 115134
Main Authors: Abdel-Basset, Mohamed, El-Shahat, Doaa, Sallam, Karam M., Munasinghe, Kumudu
Format: Article
Language:English
Subjects:
Algorithms
Gorilla troops optimizer
Gorillas
Heuristic methods
Mathematical models
Mutation
Parameter identification
Parameters
Photovoltaic cells
Photovoltaic models
Photovoltaics
Solar cell
Statistical analysis
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Summary:•A Memory-based Improved Gorilla Troops Optimizer (MIGTO) is proposed.•Two improvements are introduced to the proposed MIGTO algorithm.•MIGTO is used to determine unknown parameters of photovoltaic model.•Four different PV models are evaluated using one, two, three diode and PV models.•Comprehensive experimental results were conducted to test the efficiency of MIGTO. The parameter extraction of the PV model is a challenging issue owing to its multi-model and nonlinear characteristics. Moreover, these characteristics of the problem render the algorithms tackling it susceptible to being stuck in local optima. Nevertheless, it is imperative to accurately estimate the parameters due to their significant impact on the efficiency of the Photovoltaic (PV) system in terms of current and power generation. Hence, this paper proposes a new metaheuristic algorithm called the Memory-based Improved Gorilla Troops Optimizer (MIGTO). Furthermore, the Explorative Gorilla with an Adaptive Mutation Mechanism (EGAMM) and the Gorilla memory-saving technique are two major improvements that support the MIGTO superiority. EGAMM primarily comprises two main operators (explorative gorilla operator, adaptive mutation operator). The explorative gorilla operator enhances the search capability to explore new gorillas’ positions in the search space to escape from local optima with a small probability of 0.1. Otherwise, MIGTO updates the new gorilla position using the adaptive mutation operator by selecting two random gorillas. Additionally, the GMS technique promotes the quality of the gorillas by keeping a history of the previous positions to be compared to the current ones. Several experiments are conducted between MIGTO and other well-known metaheuristic algorithms. The experimental results and statistical analyses prove the outperformance of MIGTO in effectively extracting the parameters of different PV models, including the single diode model, the double diode model, the triple diode model, and the photovoltaic module model. Additionally, the performance of the algorithms is tested using the manufacturer’s datasheet.
ISSN:0196-8904
1879-2227
DOI:10.1016/j.enconman.2021.115134